Incidence, treatment patterns, and mortality for patients with breast cancer during the first year of the COVID-19 pandemic: a population-based study.

Purpose: The COVID-19 pandemic created significant disruptions in the diagnosis and treatment of breast cancer (BC). Several public health measures were taken with limited evidence on their potential impact. In this observational study, we sought to compare the incidence of BC, treatment patterns, and mortality during 2020 versus 2018 and 2019.

Use of Patient-Reported Outcomes in Risk Prediction Model Development to Support Cancer Care Delivery: A Scoping Review.

Purpose: The integration of patient-reported outcomes (PROs) into electronic health records (EHRs) has enabled systematic collection of symptom data to manage post-treatment symptoms. The use and integration of PRO data into routine care are associated with overall treatment success, adherence, and satisfaction. Clinical trials have demonstrated the prognostic value of PROs including physical function and global health status in predicting survival.

Changes in Cyanobacterial Phytoplankton Communities in Lake-Water Mesocosms Treated with Either Glucose or Hydrogen Peroxide.

When cyanobacterial phytoplankton form harmful cyanobacterial blooms (HCBs), the toxins they produce threaten freshwater ecosystems. Hydrogen peroxide is often used to control HCBs, but it is broadly toxic and dangerous to handle. Previously, we demonstrated that glucose addition to lake water could suppress the abundance of cyanobacteria.

Optimal Adjuvant Chemotherapy and Targeted Therapy for Early Breast Cancer-Cyclin-Dependent Kinase 4 and 6 Inhibitors: ASCO Guideline Rapid Recommendation Update.

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Incidence and Presenting Characteristics of Angiosarcoma in the US, 2001-2020.

Importance: Angiosarcoma is an aggressive vascular malignant neoplasm presenting either as a primary or secondary cancer, often arising after radiotherapy or in the context of preexisting lymphedema. Comprehensive data describing its incidence and presentation patterns are needed.

Objective: To describe the incidence, presenting characteristics, and change over time of angiosarcoma in the US.

Artificial Intelligence in Oncology: Current Landscape, Challenges, and Future Directions.

Unlabelled: Artificial intelligence (AI) in oncology is advancing beyond algorithm development to integration into clinical practice. This review describes the current state of the field, with a specific focus on clinical integration. AI applications are structured according to cancer type and clinical domain, focusing on the four most common cancers and tasks of detection, diagnosis, and treatment.

Identifying Oncology Clinical Trial Candidates Using Artificial Intelligence Predictions of Treatment Change: A Pilot Implementation Study.

Purpose: Precision oncology clinical trials often struggle to accrue, partly because it is difficult to find potentially eligible patients at moments when they need new treatment. We piloted deployment of artificial intelligence tools to identify such patients at a large academic cancer center.

Patients And Methods: Neural networks that process radiology reports to identify patients likely to start new systemic therapy were applied prospectively for patients with solid tumors that had undergone next-generation sequencing at our center.

Strategies for Implementing an Electronic Patient-Reported Outcomes-Based Symptom Management Program Across Six Cancer Centers.

Background: Electronic patient-reported outcome (ePRO)-based symptom management improves cancer patients' outcomes. However, implementation of ePROs is challenging, requiring technical resources for integration into clinical systems, substantial buy-in from clinicians and patients, novel workflows to support between-visit symptom management, and institutional investment.

Methods: The SIMPRO Research Consortium developed eSyM, an electronic health record-integrated, ePRO-based symptom management program for medical oncology and surgery patients and deployed it at six cancer centers between August 2019 and April 2022 in a type II hybrid effectiveness-implementation cluster randomized stepped-wedge study.

Lyotropic liquid crystal phases of monoolein in protic ionic liquids.

Monoolein-based liquid crystal phases are established media that are researched for various biological applications, including drug delivery. While water is the most common solvent for self-assembly, some ionic liquids (ILs) can support lipidic self-assembly. However, currently, there is limited knowledge of IL-lipid phase behavior in ILs.

The Longitudinal Implementation Strategy Tracking System (LISTS): feasibility, usability, and pilot testing of a novel method.

Background: Systematic approaches are needed to accurately characterize the dynamic use of implementation strategies and how they change over time. We describe the development and preliminary evaluation of the Longitudinal Implementation Strategy Tracking System (LISTS), a novel methodology to document and characterize implementation strategies use over time.

Methods: The development and initial evaluation of the LISTS method was conducted within the Improving the Management of SymPtoms during And following Cancer Treatment (IMPACT) Research Consortium (supported by funding provided through the NCI Cancer Moonshot).

Assessing Patient Readiness for an Electronic Patient-Reported Outcome-Based Symptom Management Intervention in a Multisite Study.

Purpose: While the use of electronic patient-reported outcomes (ePROs) in routine clinical practice is increasing, barriers to patient engagement limit adoption. Studies have focused on technology access as a key barrier, yet other characteristics may also confound readiness to use ePROs including patients' confidence in using technology and confidence in asking clinicians questions.

Methods: To assess readiness to use ePROs, adult patients from six US-based health systems who started a new oncology treatment or underwent a cancer-directed surgery were invited to complete a survey that assessed access to and confidence in the use of technology, ease of asking clinicians questions about health, and symptom management self-efficacy.

Disparities in electronic health record portal access and use among patients with cancer.

Background: Electronic health record-linked portals may improve health-care quality for patients with cancer. Barriers to portal access and use undermine interventions that rely on portals to reduce cancer care disparities. This study examined portal access and persistence of portal use and associations with patient and structural factors before the implementation of 3 portal-based interventions within the Improving the Management of symPtoms during And following Cancer Treatment (IMPACT) Consortium.

Feasibility of Weekly Electronic Health Record-Embedded Patient-Reported Outcomes for Patients Starting Oral Cancer-Directed Therapy.

Purpose: To examine the feasibility of integrating a symptom management platform into the electronic health record (EHR) using electronic patient-reported outcomes (ePROs) during oral cancer-directed therapy (OCDT) and explore the impact of prompting oncology nurse navigators (ONNs) to respond to severe symptomatic adverse events (SAEs).

Materials And Methods: Adults prescribed OCDT at Dana-Farber Cancer Institute were consecutively invited to participate. Participants received weekly messages to complete ePROs.

Machine learning for genetics-based classification and treatment response prediction in cancer of unknown primary.

Cancer of unknown primary (CUP) is a type of cancer that cannot be traced back to its primary site and accounts for 3-5% of all cancers. Established targeted therapies are lacking for CUP, leading to generally poor outcomes. We developed OncoNPC, a machine-learning classifier trained on targeted next-generation sequencing (NGS) data from 36,445 tumors across 22 cancer types from three institutions.